Preparing

library(readxl)
library(tidyverse)
## -- Attaching packages ------------------------------------------------ tidyverse 1.2.1 --
## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0
## -- Conflicts --------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(stringr)
setwd("C:/Users/student/Documents/MATH 421")
c2015<- read_excel("c2015.xlsx")
c2015 <- na.omit(c2015)
c2015 <- c2015 %>% filter_all(~!(.=="Unknown"))
c2015 <- c2015 %>% filter_all(~!(.=="Not Rep"))
c2015 <- c2015 %>% filter_all(~!(.==str_detect(.,"Not Rep")))
c2015 <- c2015 %>% filter_all(~!(.==str_detect(.,"Unknown")))
c2015 <- c2015 %>% filter_all(~!(.=="Not Reported"))
c2015$TRAV_SP <- c2015$TRAV_SP %>% str_replace("MPH", "")
c2015$TRAV_SP <- c2015$TRAV_SP %>% str_replace("Stopped", "0")
c2015$AGE <- c2015$AGE %>% str_replace("Less than 1", "0")
c2015$TRAV_SP <- as.numeric(c2015$TRAV_SP)
c2015$AGE <- as.numeric(c2015$AGE)
c2015 <- c2015 %>% filter(SEAT_POS == "Front Seat, Left Side")
library("gganimate")
library(gifski)
library("png")

1

c2015 %>% group_by(SEX, INJ_SEV, MONTH) %>% summarize(mean = mean(TRAV_SP)) %>%
  mutate(MONTH1=factor(MONTH, levels=month.name)) %>% 
  mutate(speed_above_below = ifelse(mean > mean(c2015$TRAV_SP), "above", "below"), standardized_speed = (mean - mean(c2015$TRAV_SP))/sd(c2015$TRAV_SP)) %>% 
      ggplot(aes(MONTH1, standardized_speed, label = standardized_speed)) + geom_bar(stat = "identity", aes(fill = speed_above_below), width=0.5) +
  labs(title = "MONTH = {closest_state}", subtitle = "Normalized Monthly Average Speed", x="MONTH") + 
  coord_flip() +
    transition_states(MONTH1)

2

c2015 %>% mutate(MONTH1=factor(MONTH, levels=month.name)) %>% 
  group_by(MONTH1, DRINKING, SEX) %>% 
  summarize(counting = n()) %>% 
  ungroup() %>% 
  group_by(MONTH1) %>% 
  mutate(total = sum(counting)) %>% 
  mutate(proportion = round(counting/total,2)) %>% 
  ungroup() %>% 
  group_by(MONTH1, DRINKING, SEX) %>% 
  ggplot(aes(x=DRINKING, y=counting, fill = SEX)) + 
  geom_col(position = "dodge") + 
  geom_text(aes(label=proportion),position = position_dodge(0.9)) +
  transition_states(MONTH1) +
  labs(title = "MONTH = {closest_state}")

3

debt <- tibble::tribble(
          ~Quarter, ~Mortgage, ~HE.Revolving, ~Auto.Loan, ~Credit.Card, ~Student.Loan, ~Other, ~Total,
           "03:Q1",      4.94,          0.24,       0.64,         0.69,          0.24,   0.48,   7.23,
           "03:Q2",      5.08,          0.26,       0.62,         0.69,          0.24,   0.49,   7.38,
           "03:Q3",      5.18,          0.27,       0.68,         0.69,          0.25,   0.48,   7.56,
           "03:Q4",      5.66,           0.3,        0.7,          0.7,          0.25,   0.45,   8.07,
           "04:Q1",      5.84,          0.33,       0.72,          0.7,          0.26,   0.45,   8.29,
           "04:Q2",      5.97,          0.37,       0.74,          0.7,          0.26,   0.42,   8.46,
           "04:Q3",      6.21,          0.43,       0.75,         0.71,          0.33,   0.41,   8.83,
           "04:Q4",      6.36,          0.47,       0.73,         0.72,          0.35,   0.42,   9.04,
           "05:Q1",      6.51,           0.5,       0.73,         0.71,          0.36,   0.39,   9.21,
           "05:Q2",       6.7,          0.53,       0.77,         0.72,          0.37,    0.4,   9.49,
           "05:Q3",      6.91,          0.54,       0.83,         0.73,          0.38,   0.41,   9.79,
           "05:Q4",       7.1,          0.57,       0.79,         0.74,          0.39,   0.42,     10,
           "06:Q1",      7.44,          0.58,       0.79,         0.72,          0.43,   0.42,  10.38,
           "06:Q2",      7.76,          0.59,        0.8,         0.74,          0.44,   0.42,  10.75,
           "06:Q3",      8.05,           0.6,       0.82,         0.75,          0.45,   0.44,  11.11,
           "06:Q4",      8.23,           0.6,       0.82,         0.77,          0.48,   0.41,  11.31,
           "07:Q1",      8.42,          0.61,       0.79,         0.76,          0.51,    0.4,   11.5,
           "07:Q2",      8.71,          0.62,       0.81,          0.8,          0.51,   0.41,  11.85,
           "07:Q3",      8.93,          0.63,       0.82,         0.82,          0.53,   0.41,  12.13,
           "07:Q4",       9.1,          0.65,       0.82,         0.84,          0.55,   0.42,  12.37,
           "08:Q1",      9.23,          0.66,       0.81,         0.84,          0.58,   0.42,  12.54,
           "08:Q2",      9.27,          0.68,       0.81,         0.85,          0.59,    0.4,   12.6,
           "08:Q3",      9.29,          0.69,       0.81,         0.86,          0.61,   0.41,  12.68,
           "08:Q4",      9.26,          0.71,       0.79,         0.87,          0.64,   0.41,  12.67,
           "09:Q1",      9.14,          0.71,       0.77,         0.84,          0.66,   0.41,  12.53,
           "09:Q2",      9.06,          0.71,       0.74,         0.82,          0.68,   0.39,  12.41,
           "09:Q3",      8.94,          0.71,       0.74,         0.81,          0.69,   0.38,  12.28,
           "09:Q4",      8.84,          0.71,       0.72,          0.8,          0.72,   0.38,  12.17,
           "10:Q1",      8.83,           0.7,        0.7,         0.76,          0.76,   0.36,  12.12,
           "10:Q2",       8.7,          0.68,        0.7,         0.74,          0.76,   0.35,  11.94,
           "10:Q3",      8.61,          0.67,       0.71,         0.73,          0.78,   0.34,  11.84,
           "10:Q4",      8.45,          0.67,       0.71,         0.73,          0.81,   0.34,  11.71,
           "11:Q1",      8.54,          0.64,       0.71,          0.7,          0.84,   0.33,  11.75,
           "11:Q2",      8.52,          0.62,       0.71,         0.69,          0.85,   0.33,  11.73,
           "11:Q3",       8.4,          0.64,       0.73,         0.69,          0.87,   0.33,  11.66,
           "11:Q4",      8.27,          0.63,       0.73,          0.7,          0.87,   0.33,  11.54,
           "12:Q1",      8.19,          0.61,       0.74,         0.68,           0.9,   0.32,  11.44,
           "12:Q2",      8.15,          0.59,       0.75,         0.67,          0.91,   0.31,  11.38,
           "12:Q3",      8.03,          0.57,       0.77,         0.67,          0.96,   0.31,  11.31,
           "12:Q4",      8.03,          0.56,       0.78,         0.68,          0.97,   0.32,  11.34,
           "13:Q1",      7.93,          0.55,       0.79,         0.66,          0.99,   0.31,  11.23,
           "13:Q2",      7.84,          0.54,       0.81,         0.67,          0.99,    0.3,  11.15,
           "13:Q3",       7.9,          0.54,       0.85,         0.67,          1.03,    0.3,  11.28,
           "13:Q4",      8.05,          0.53,       0.86,         0.68,          1.08,   0.32,  11.52,
           "14:Q1",      8.17,          0.53,       0.88,         0.66,          1.11,   0.31,  11.65,
           "14:Q2",       8.1,          0.52,       0.91,         0.67,          1.12,   0.32,  11.63,
           "14:Q3",      8.13,          0.51,       0.93,         0.68,          1.13,   0.33,  11.71,
           "14:Q4",      8.17,          0.51,       0.96,          0.7,          1.16,   0.34,  11.83,
           "15:Q1",      8.17,          0.51,       0.97,         0.68,          1.19,   0.33,  11.85,
           "15:Q2",      8.12,           0.5,       1.01,          0.7,          1.19,   0.34,  11.85,
           "15:Q3",      8.26,          0.49,       1.05,         0.71,           1.2,   0.35,  12.07,
           "15:Q4",      8.25,          0.49,       1.06,         0.73,          1.23,   0.35,  12.12,
           "16:Q1",      8.37,          0.49,       1.07,         0.71,          1.26,   0.35,  12.25,
           "16:Q2",      8.36,          0.48,        1.1,         0.73,          1.26,   0.36,  12.29,
           "16:Q3",      8.35,          0.47,       1.14,         0.75,          1.28,   0.37,  12.35,
           "16:Q4",      8.48,          0.47,       1.16,         0.78,          1.31,   0.38,  12.58,
           "17:Q1",      8.63,          0.46,       1.17,         0.76,          1.34,   0.37,  12.73,
           "17:Q2",      8.69,          0.45,       1.19,         0.78,          1.34,   0.38,  12.84,
           "17:Q3",      8.74,          0.45,       1.21,         0.81,          1.36,   0.39,  12.96,
           "17:Q4",      8.88,          0.44,       1.22,         0.83,          1.38,   0.39,  13.15,
           "18:Q1",      8.94,          0.44,       1.23,         0.82,          1.41,   0.39,  13.21,
           "18:Q2",         9,          0.43,       1.24,         0.83,          1.41,   0.39,  13.29,
           "18:Q3",      9.14,          0.42,       1.27,         0.84,          1.44,    0.4,  13.51,
           "18:Q4",      9.12,          0.41,       1.27,         0.87,          1.46,   0.41,  13.54,
           "19:Q1",      9.24,          0.41,       1.28,         0.85,          1.49,    0.4,  13.67,
           "19:Q2",      9.41,           0.4,        1.3,         0.87,          1.48,   0.41,  13.86
)

debt %>% ggplot(aes(Student.Loan, Credit.Card)) +
  geom_line()

4

debt %>% ggplot(aes(Student.Loan, Credit.Card)) +
  geom_line() +
  geom_segment(aes(xend=1.5, yend=Credit.Card)) +
  geom_point() +
  geom_text(aes(x=1.6, label=Credit.Card)) +
  transition_reveal(Student.Loan) +
  ease_aes()

5

debt %>% mutate(date = seq(as.Date("2003/01/01"), by="quarter", length.out = 66)) %>% 
  ggplot(aes(date, Student.Loan)) +
  geom_line()

6

debt %>% mutate(date = seq(as.Date("2003/01/01"), by="quarter", length.out = 66)) %>%   ggplot(aes(date, Student.Loan)) +
  geom_line() +
  transition_reveal(date) +
  ease_aes()

7

debt %>% mutate(date = seq(as.Date("2003/01/01"), by="quarter", length.out = 66)) %>%   ggplot(aes(date, Student.Loan)) +
  geom_line() +
  geom_segment(aes(xend=as.Date("2019/04/01"), yend=Student.Loan)) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/06/01"), label=Student.Loan)) +
  transition_reveal(date) +
  ease_aes()

8

debt %>%  mutate(Quarter1 = gsub("^.*?:","",debt$Quarter)) %>%
  select(-Quarter) %>%
  mutate(date = seq(as.Date("2003/01/01"), by="quarter", length.out = 66)) %>% 
  gather(Debt_Category, Debt_Amount, Mortgage:Other) %>% 
  ggplot(aes(date, Debt_Amount, col=Debt_Category)) +
  geom_line() +
  geom_segment(aes(xend=as.Date("2019/04/01"), yend=Debt_Amount), linetype=2) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/06/01"), label=Debt_Category), hjust=0) +
  coord_cartesian(clip = 'off') +
  transition_reveal(date) +
  ease_aes()

9.1

# Mortgage accounts for the most of the total debt
# Mortgage VS Total
debt %>%  mutate(Quarter1 = gsub("^.*?:","",debt$Quarter)) %>%
  select(-Quarter) %>% 
  mutate(date = seq(as.Date("2003/01/01"), by="quarter", length.out = 66)) %>%
  gather(Debt_Category, Debt_Amount, c(Mortgage,Total)) %>%
  ggplot(aes(date, Debt_Amount, col=Debt_Category)) +
  geom_line() +
  geom_segment(aes(xend=as.Date("2019/04/01"), yend=Debt_Amount), linetype=2) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/06/01"), label=Debt_Category), hjust=0) +
  coord_cartesian(clip = 'off') +
  transition_reveal(date) +
  ease_aes() +
  labs(title = "Mortgage VS Total Debt in the U.S.", x="Date", y="Debt in Trillion Dollars")

9.2

# Everything Else
debt %>%  mutate(Quarter1 = gsub("^.*?:","",debt$Quarter)) %>%
  select(-Quarter, -Total, -Mortgage) %>% 
  mutate(date = seq(as.Date("2003/01/01"), by="quarter", length.out = 66)) %>%
  gather(Debt_Category, Debt_Amount, HE.Revolving:Other) %>%
  ggplot(aes(date, Debt_Amount, col=Debt_Category)) +
  geom_line() +
  geom_segment(aes(xend=as.Date("2019/04/01"), yend=Debt_Amount), linetype=2) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/06/01"), label=Debt_Category), hjust=0) +
  coord_cartesian(clip = 'off') +
  transition_reveal(date) +
  ease_aes() +
  labs(title = "Various Debts in the U.S.", x="Date", y="Debt in Trillion Dollars") +
  theme(plot.margin=margin(1,2,1,1,"cm"), legend.position = "bottom")

### Mortgage has been the biggest factor of the total debt in the U.S. for a long time. Student loan has been soaring, even during the great recession. Debts generally decreased for awhile after the great recession arount 2008 - 2009. However, soon after the great recession, debts start to rise again. On the other hand, revolving debt has bee steadily decreasing.

10.1

gdp <- tibble::tribble(
                ~V1,    ~V2,
         "Mar-2000", 12.924,
         "Jun-2000", 13.161,
         "Sep-2000", 13.178,
         "Dec-2000", 13.261,
         "Mar-2001", 13.223,
         "Jun-2001",   13.3,
         "Sep-2001", 13.245,
         "Dec-2001", 13.281,
         "Mar-2002", 13.397,
         "Jun-2002", 13.478,
         "Sep-2002", 13.538,
         "Dec-2002", 13.559,
         "Mar-2003", 13.634,
         "Jun-2003", 13.752,
         "Sep-2003", 13.985,
         "Dec-2003", 14.146,
         "Mar-2004", 14.221,
         "Jun-2004",  14.33,
         "Sep-2004", 14.465,
         "Dec-2004",  14.61,
         "Mar-2005", 14.772,
         "Jun-2005",  14.84,
         "Sep-2005", 14.972,
         "Dec-2005", 15.067,
         "Mar-2006", 15.267,
         "Jun-2006", 15.303,
         "Sep-2006", 15.326,
         "Dec-2006", 15.457,
         "Mar-2007", 15.493,
         "Jun-2007", 15.582,
         "Sep-2007", 15.667,
         "Dec-2007", 15.762,
         "Mar-2008", 15.671,
         "Jun-2008", 15.752,
         "Sep-2008", 15.667,
         "Dec-2008", 15.328,
         "Mar-2009", 15.156,
         "Jun-2009", 15.134,
         "Sep-2009", 15.189,
         "Dec-2009", 15.356,
         "Mar-2010", 15.415,
         "Jun-2010", 15.557,
         "Sep-2010", 15.672,
         "Dec-2010", 15.751,
         "Mar-2011", 15.713,
         "Jun-2011", 15.825,
         "Sep-2011", 15.821,
         "Dec-2011", 16.004,
         "Mar-2012", 16.129,
         "Jun-2012", 16.199,
         "Sep-2012", 16.221,
         "Dec-2012", 16.239,
         "Mar-2013", 16.383,
         "Jun-2013", 16.403,
         "Sep-2013", 16.532,
         "Dec-2013", 16.664,
         "Mar-2014", 16.617,
         "Jun-2014", 16.842,
         "Sep-2014", 17.047,
         "Dec-2014", 17.143,
         "Mar-2015", 17.278,
         "Jun-2015", 17.406,
         "Sep-2015", 17.463,
         "Dec-2015", 17.469,
         "Mar-2016", 17.557,
         "Jun-2016", 17.639,
         "Sep-2016", 17.735,
         "Dec-2016", 17.824,
         "Mar-2017", 17.925,
         "Jun-2017", 18.021,
         "Sep-2017", 18.164,
         "Dec-2017", 18.323,
         "Mar-2018", 18.438,
         "Jun-2018", 18.598,
         "Sep-2018", 18.733,
         "Dec-2018", 18.784,
         "Mar-2019", 18.927,
         "Jun-2019", 19.024
         )
names(gdp) <- c("quarters", "GDP")

gdp %>% mutate(date = seq(as.Date("2000/03/01"), by="quarter", length.out = 78)) %>% ggplot(aes(date, GDP, col="red")) + 
  geom_segment(aes(xend=as.Date("2019/06/01"), yend=GDP), linetype=2) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/09/01"), label=date), hjust=0) +
  coord_cartesian(clip = 'off') +
  transition_states(date) +
  labs(title = "Changes in GDP", x="Year", y="GDP in Millions of Dollars, Chained 2012") +
  theme(legend.position="none", plot.margin=margin(1,2,1,1,"cm"))

10.2

labor <- tibble::tribble(
                  ~V1,  ~V2,  ~V3,
           "Jan-2000",    4, 67.3,
           "Feb-2000",  4.1, 67.3,
           "Mar-2000",    4, 67.3,
           "Apr-2000",  3.8, 67.3,
           "May-2000",    4, 67.1,
           "Jun-2000",    4, 67.1,
           "Jul-2000",    4, 66.9,
           "Aug-2000",  4.1, 66.9,
           "Sep-2000",  3.9, 66.9,
           "Oct-2000",  3.9, 66.8,
           "Nov-2000",  3.9, 66.9,
           "Dec-2000",  3.9,   67,
           "Jan-2001",  4.2, 67.2,
           "Feb-2001",  4.2, 67.1,
           "Mar-2001",  4.3, 67.2,
           "Apr-2001",  4.4, 66.9,
           "May-2001",  4.3, 66.7,
           "Jun-2001",  4.5, 66.7,
           "Jul-2001",  4.6, 66.8,
           "Aug-2001",  4.9, 66.5,
           "Sep-2001",    5, 66.8,
           "Oct-2001",  5.3, 66.7,
           "Nov-2001",  5.5, 66.7,
           "Dec-2001",  5.7, 66.7,
           "Jan-2002",  5.7, 66.5,
           "Feb-2002",  5.7, 66.8,
           "Mar-2002",  5.7, 66.6,
           "Apr-2002",  5.9, 66.7,
           "May-2002",  5.8, 66.7,
           "Jun-2002",  5.8, 66.6,
           "Jul-2002",  5.8, 66.5,
           "Aug-2002",  5.7, 66.6,
           "Sep-2002",  5.7, 66.7,
           "Oct-2002",  5.7, 66.6,
           "Nov-2002",  5.9, 66.4,
           "Dec-2002",    6, 66.3,
           "Jan-2003",  5.8, 66.4,
           "Feb-2003",  5.9, 66.4,
           "Mar-2003",  5.9, 66.3,
           "Apr-2003",    6, 66.4,
           "May-2003",  6.1, 66.4,
           "Jun-2003",  6.3, 66.5,
           "Jul-2003",  6.2, 66.2,
           "Aug-2003",  6.1, 66.1,
           "Sep-2003",  6.1, 66.1,
           "Oct-2003",    6, 66.1,
           "Nov-2003",  5.8, 66.1,
           "Dec-2003",  5.7, 65.9,
           "Jan-2004",  5.7, 66.1,
           "Feb-2004",  5.6,   66,
           "Mar-2004",  5.8,   66,
           "Apr-2004",  5.6, 65.9,
           "May-2004",  5.6,   66,
           "Jun-2004",  5.6, 66.1,
           "Jul-2004",  5.5, 66.1,
           "Aug-2004",  5.4,   66,
           "Sep-2004",  5.4, 65.8,
           "Oct-2004",  5.5, 65.9,
           "Nov-2004",  5.4,   66,
           "Dec-2004",  5.4, 65.9,
           "Jan-2005",  5.3, 65.8,
           "Feb-2005",  5.4, 65.9,
           "Mar-2005",  5.2, 65.9,
           "Apr-2005",  5.2, 66.1,
           "May-2005",  5.1, 66.1,
           "Jun-2005",    5, 66.1,
           "Jul-2005",    5, 66.1,
           "Aug-2005",  4.9, 66.2,
           "Sep-2005",    5, 66.1,
           "Oct-2005",    5, 66.1,
           "Nov-2005",    5,   66,
           "Dec-2005",  4.9,   66,
           "Jan-2006",  4.7,   66,
           "Feb-2006",  4.8, 66.1,
           "Mar-2006",  4.7, 66.2,
           "Apr-2006",  4.7, 66.1,
           "May-2006",  4.6, 66.1,
           "Jun-2006",  4.6, 66.2,
           "Jul-2006",  4.7, 66.1,
           "Aug-2006",  4.7, 66.2,
           "Sep-2006",  4.5, 66.1,
           "Oct-2006",  4.4, 66.2,
           "Nov-2006",  4.5, 66.3,
           "Dec-2006",  4.4, 66.4,
           "Jan-2007",  4.6, 66.4,
           "Feb-2007",  4.5, 66.3,
           "Mar-2007",  4.4, 66.2,
           "Apr-2007",  4.5, 65.9,
           "May-2007",  4.4,   66,
           "Jun-2007",  4.6,   66,
           "Jul-2007",  4.7,   66,
           "Aug-2007",  4.6, 65.8,
           "Sep-2007",  4.7,   66,
           "Oct-2007",  4.7, 65.8,
           "Nov-2007",  4.7,   66,
           "Dec-2007",    5,   66,
           "Jan-2008",    5, 66.2,
           "Feb-2008",  4.9,   66,
           "Mar-2008",  5.1, 66.1,
           "Apr-2008",    5, 65.9,
           "May-2008",  5.4, 66.1,
           "Jun-2008",  5.6, 66.1,
           "Jul-2008",  5.8, 66.1,
           "Aug-2008",  6.1, 66.1,
           "Sep-2008",  6.1,   66,
           "Oct-2008",  6.5,   66,
           "Nov-2008",  6.8, 65.9,
           "Dec-2008",  7.3, 65.8,
           "Jan-2009",  7.8, 65.7,
           "Feb-2009",  8.3, 65.8,
           "Mar-2009",  8.7, 65.6,
           "Apr-2009",    9, 65.7,
           "May-2009",  9.4, 65.7,
           "Jun-2009",  9.5, 65.7,
           "Jul-2009",  9.5, 65.5,
           "Aug-2009",  9.6, 65.4,
           "Sep-2009",  9.8, 65.1,
           "Oct-2009",   10,   65,
           "Nov-2009",  9.9,   65,
           "Dec-2009",  9.9, 64.6,
           "Jan-2010",  9.8, 64.8,
           "Feb-2010",  9.8, 64.9,
           "Mar-2010",  9.9, 64.9,
           "Apr-2010",  9.9, 65.2,
           "May-2010",  9.6, 64.9,
           "Jun-2010",  9.4, 64.6,
           "Jul-2010",  9.4, 64.6,
           "Aug-2010",  9.5, 64.7,
           "Sep-2010",  9.5, 64.6,
           "Oct-2010",  9.4, 64.4,
           "Nov-2010",  9.8, 64.6,
           "Dec-2010",  9.3, 64.3,
           "Jan-2011",  9.1, 64.2,
           "Feb-2011",    9, 64.1,
           "Mar-2011",    9, 64.2,
           "Apr-2011",  9.1, 64.2,
           "May-2011",    9, 64.1,
           "Jun-2011",  9.1,   64,
           "Jul-2011",    9,   64,
           "Aug-2011",    9, 64.1,
           "Sep-2011",    9, 64.2,
           "Oct-2011",  8.8, 64.1,
           "Nov-2011",  8.6, 64.1,
           "Dec-2011",  8.5,   64,
           "Jan-2012",  8.3, 63.7,
           "Feb-2012",  8.3, 63.8,
           "Mar-2012",  8.2, 63.8,
           "Apr-2012",  8.2, 63.7,
           "May-2012",  8.2, 63.7,
           "Jun-2012",  8.2, 63.8,
           "Jul-2012",  8.2, 63.7,
           "Aug-2012",  8.1, 63.5,
           "Sep-2012",  7.8, 63.6,
           "Oct-2012",  7.8, 63.8,
           "Nov-2012",  7.7, 63.6,
           "Dec-2012",  7.9, 63.7,
           "Jan-2013",    8, 63.7,
           "Feb-2013",  7.7, 63.4,
           "Mar-2013",  7.5, 63.3,
           "Apr-2013",  7.6, 63.4,
           "May-2013",  7.5, 63.4,
           "Jun-2013",  7.5, 63.4,
           "Jul-2013",  7.3, 63.3,
           "Aug-2013",  7.2, 63.3,
           "Sep-2013",  7.2, 63.2,
           "Oct-2013",  7.2, 62.8,
           "Nov-2013",  6.9,   63,
           "Dec-2013",  6.7, 62.9,
           "Jan-2014",  6.6, 62.9,
           "Feb-2014",  6.7, 62.9,
           "Mar-2014",  6.7, 63.1,
           "Apr-2014",  6.2, 62.8,
           "May-2014",  6.3, 62.9,
           "Jun-2014",  6.1, 62.8,
           "Jul-2014",  6.2, 62.9,
           "Aug-2014",  6.1, 62.9,
           "Sep-2014",  5.9, 62.8,
           "Oct-2014",  5.7, 62.9,
           "Nov-2014",  5.8, 62.9,
           "Dec-2014",  5.6, 62.8,
           "Jan-2015",  5.7, 62.9,
           "Feb-2015",  5.5, 62.7,
           "Mar-2015",  5.4, 62.6,
           "Apr-2015",  5.4, 62.7,
           "May-2015",  5.6, 62.9,
           "Jun-2015",  5.3, 62.6,
           "Jul-2015",  5.2, 62.6,
           "Aug-2015",  5.1, 62.6,
           "Sep-2015",    5, 62.4,
           "Oct-2015",    5, 62.5,
           "Nov-2015",  5.1, 62.6,
           "Dec-2015",    5, 62.7,
           "Jan-2016",  4.9, 62.7,
           "Feb-2016",  4.9, 62.8,
           "Mar-2016",    5, 62.9,
           "Apr-2016",    5, 62.8,
           "May-2016",  4.8, 62.7,
           "Jun-2016",  4.9, 62.7,
           "Jul-2016",  4.8, 62.8,
           "Aug-2016",  4.9, 62.9,
           "Sep-2016",    5, 62.9,
           "Oct-2016",  4.9, 62.8,
           "Nov-2016",  4.7, 62.7,
           "Dec-2016",  4.7, 62.7,
           "Jan-2017",  4.7, 62.9,
           "Feb-2017",  4.7, 62.9,
           "Mar-2017",  4.4, 62.9,
           "Apr-2017",  4.4, 62.9,
           "May-2017",  4.4, 62.8,
           "Jun-2017",  4.3, 62.8,
           "Jul-2017",  4.3, 62.9,
           "Aug-2017",  4.4, 62.9,
           "Sep-2017",  4.2, 63.1,
           "Oct-2017",  4.1, 62.7,
           "Nov-2017",  4.2, 62.8,
           "Dec-2017",  4.1, 62.7,
           "Jan-2018",  4.1, 62.7,
           "Feb-2018",  4.1,   63,
           "Mar-2018",    4, 62.9,
           "Apr-2018",  3.9, 62.8,
           "May-2018",  3.8, 62.8,
           "Jun-2018",    4, 62.9,
           "Jul-2018",  3.9, 62.9,
           "Aug-2018",  3.8, 62.7,
           "Sep-2018",  3.7, 62.7,
           "Oct-2018",  3.8, 62.9,
           "Nov-2018",  3.7, 62.9,
           "Dec-2018",  3.9, 63.1,
           "Jan-2019",    4, 63.2,
           "Feb-2019",  3.8, 63.2,
           "Mar-2019",  3.8,   63,
           "Apr-2019",  3.6, 62.8,
           "May-2019",  3.6, 62.8,
           "Jun-2019",  3.7, 62.9,
           "Jul-2019",  3.7,   63,
           "Aug-2019",  3.7, 63.2
           )
names(labor) <- c("months", "UE", "LF_participation_rate")

labor %>% mutate(date = seq(as.Date("2000/01/01"), by="month", length.out = 236)) %>% gather(labor_category, labor_value, UE:LF_participation_rate) %>% 
  ggplot(aes(date, labor_value, col=labor_category)) + 
  geom_line() +
  geom_segment(aes(xend=as.Date("2019/08/01"), yend=labor_value), linetype=2) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/09/01"), label=labor_category), hjust=0) +
  coord_cartesian(clip = 'off') +
  transition_reveal(date) +
  ease_aes() +
  labs(title = "Changes in Labor Related Indicators", x="Year", y="Unemployment Rate and Labor Force Participation Rate")

10.3

housing <- tibble::tribble(
                ~V1, ~V2,
             200001, 343,
             200002, 394,
             200003, 380,
             200004, 375,
             200005, 329,
             200006, 351,
             200007, 341,
             200008, 329,
             200009, 314,
             200010, 312,
             200011, 322,
             200012, 320,
             200101, 323,
             200102, 325,
             200103, 347,
             200104, 352,
             200105, 343,
             200106, 333,
             200107, 344,
             200108, 331,
             200109, 324,
             200110, 300,
             200111, 326,
             200112, 314,
             200201, 340,
             200202, 330,
             200203, 353,
             200204, 331,
             200205, 342,
             200206, 342,
             200207, 348,
             200208, 356,
             200209, 359,
             200210, 347,
             200211, 338,
             200212, 335,
             200301, 341,
             200302, 331,
             200303, 324,
             200304, 308,
             200305, 319,
             200306, 328,
             200307, 359,
             200308, 355,
             200309, 365,
             200310, 356,
             200311, 370,
             200312, 378,
             200401, 383,
             200402, 375,
             200403, 361,
             200404, 363,
             200405, 351,
             200406, 329,
             200407, 319,
             200408, 321,
             200409, 337,
             200410, 365,
             200411, 362,
             200412, 355,
             200501, 352,
             200502, 383,
             200503, 367,
             200504, 366,
             200505, 332,
             200506, 354,
             200507, 330,
             200508, 349,
             200509, 353,
             200510, 351,
             200511, 342,
             200512, 343,
             200601, 385,
             200602, 377,
             200603, 378,
             200604, 331,
             200605, 350,
             200606, 344,
             200607, 345,
             200608, 317,
             200609, 312,
             200610, 300,
             200611, 298,
             200612, 320,
             200701, 320,
             200702, 323,
             200703, 288,
             200704, 292,
             200705, 290,
             200706, 298,
             200707, 305,
             200708, 334,
             200709, 311,
             200710, 336,
             200711, 333,
             200712, 327,
             200801, 302,
             200802, 307,
             200803, 322,
             200804, 329,
             200805, 301,
             200806, 341,
             200807, 334,
             200808, 315,
             200809, 276,
             200810, 252,
             200811, 237,
             200812, 195,
             200901, 161,
             200902, 171,
             200903, 169,
             200904, 156,
             200905, 125,
             200906, 109,
             200907, 107,
             200908,  96,
             200909,  86,
             200910,  77,
             200911,  75,
             200912,  82,
             201001,  97,
             201002,  93,
             201003,  92,
             201004,  98,
             201005, 114,
             201006, 113,
             201007, 112,
             201008, 131,
             201009, 149,
             201010, 144,
             201011, 114,
             201012, 103,
             201101, 134,
             201102, 145,
             201103, 166,
             201104, 145,
             201105, 152,
             201106, 153,
             201107, 170,
             201108, 176,
             201109, 195,
             201110, 189,
             201111, 216,
             201112, 196,
             201201, 208,
             201202, 205,
             201203, 222,
             201204, 237,
             201205, 221,
             201206, 224,
             201207, 215,
             201208, 225,
             201209, 231,
             201210, 258,
             201211, 274,
             201212, 310,
             201301, 303,
             201302, 319,
             201303, 327,
             201304, 315,
             201305, 319,
             201306, 265,
             201307, 278,
             201308, 266,
             201309, 282,
             201310, 296,
             201311, 335,
             201312, 362,
             201401, 359,
             201402, 342,
             201403, 330,
             201404, 355,
             201405, 357,
             201406, 353,
             201407, 364,
             201408, 359,
             201409, 376,
             201410, 358,
             201411, 359,
             201412, 357,
             201501, 360,
             201502, 347,
             201503, 341,
             201504, 363,
             201505, 388,
             201506, 443,
             201507, 420,
             201508, 427,
             201509, 416,
             201510, 407,
             201511, 405,
             201512, 378,
             201601, 377,
             201602, 369,
             201603, 359,
             201604, 373,
             201605, 381,
             201606, 403,
             201607, 426,
             201608, 440,
             201609, 393,
             201610, 392,
             201611, 354,
             201612, 415,
             201701, 403,
             201702, 432,
             201703, 395,
             201704, 364,
             201705, 337,
             201706, 345,
             201707, 354,
             201708, 341,
             201709, 324,
             201710, 332,
             201711, 351,
             201712, 363,
             201801, 388,
             201802, 402,
             201803, 428,
             201804, 402,
             201805, 404,
             201806, 365,
             201807, 348,
             201808, 347,
             201809, 357,
             201810, 364,
             201811, 367,
             201812, 357,
             201901, 350,
             201902, 337,
             201903, 349,
             201904, 377,
             201905, 408,
             201906, 411,
             201907, 380
             )

names(housing) <- c("month", "housing_start")

variables <- cbind(labor[-236,], housing)
variables %>% mutate(date = seq(as.Date("2000/01/01"), by="month", length.out = 235)) %>% select(-months, -month) %>% mutate(UE1 = UE*100) %>% 
  gather(indicator, indicator_value, c(UE1,housing_start)) %>% 
  ggplot(aes(date, indicator_value, col=indicator)) + 
  geom_line() +
  geom_segment(aes(xend=as.Date("2019/08/01"), yend=indicator_value), linetype=2) +
  geom_point() +
  geom_text(aes(x=as.Date("2019/09/01"), label=indicator), hjust=0) +
  coord_cartesian(clip = 'off') +
  transition_reveal(date) +
  ease_aes() +
  labs(title = "Unemployment Rate *100 and Housing Start", x="Year", y="Unemployment Rate and Housing Start (Multifamily, 3-month moving average)")